chr6.19841_chr6_135584481_135585458_-_1.R fitVsDatCorrelation=0.90106170604179 cont.fitVsDatCorrelation=0.295713295335043 fstatistic=6461.51851557231,36,324 cont.fstatistic=1324.64828339845,36,324 residuals=-0.600503351918273,-0.0882217931639267,-0.000476009957783309,0.0839066363216885,0.603800691239427 cont.residuals=-0.793520589405392,-0.23436735569355,-0.0862061983638761,0.141863922079470,1.06210075169234 predictedValues: Include Exclude Both chr6.19841_chr6_135584481_135585458_-_1.R.tl.Lung 73.3210661670232 60.222849826012 63.1738438502278 chr6.19841_chr6_135584481_135585458_-_1.R.tl.cerebhem 72.1721852155668 85.7438344950118 58.2971105880865 chr6.19841_chr6_135584481_135585458_-_1.R.tl.cortex 68.3347845851152 64.0646867632207 58.2358371807536 chr6.19841_chr6_135584481_135585458_-_1.R.tl.heart 78.7386030284504 64.7132134230748 64.962241079728 chr6.19841_chr6_135584481_135585458_-_1.R.tl.kidney 75.4575538601724 58.4507178774756 62.7089853729116 chr6.19841_chr6_135584481_135585458_-_1.R.tl.liver 70.545370517036 59.4465032831061 58.3350415186057 chr6.19841_chr6_135584481_135585458_-_1.R.tl.stomach 78.0968437051263 74.9119783652024 58.2278333918172 chr6.19841_chr6_135584481_135585458_-_1.R.tl.testicle 72.0641990595557 72.7468277335112 67.5725215122597 diffExp=13.0982163410112,-13.5716492794451,4.27009782189444,14.0253896053756,17.0068359826969,11.0988672339300,3.18486533992396,-0.682628673955506 diffExpScore=1.55651545699346 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=1,0,0,1,1,0,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 72.6483707286561 74.1517131172838 71.861434469337 cerebhem 73.6797969291774 68.460289971938 58.6143361815315 cortex 66.1238564709767 74.0969264976303 89.803424995117 heart 63.2546377857103 66.7834410335269 91.4941197512742 kidney 68.4776019326087 65.5269806334842 78.9347961846325 liver 79.8129561208418 68.364497351231 70.7352027635631 stomach 67.8191070767443 64.76940638418 61.5308106477668 testicle 65.642515742484 76.9002148593581 61.5631632960279 cont.diffExp=-1.50334238862769,5.21950695723942,-7.97307002665359,-3.52880324781665,2.95062129912452,11.4484587696108,3.04970069256431,-11.2576991168741 cont.diffExpScore=18.0878412917624 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=0.0531498304425816 cont.tran.correlation=-0.0997419266251197 tran.covariance=0.000443473012837205 cont.tran.covariance=-0.000415205274734164 tran.mean=70.5644511190413 cont.tran.mean=69.7820195397395 weightedLogRatios: wLogRatio Lung 0.825845053324013 cerebhem -0.752166871475195 cortex 0.27050149131303 heart 0.83725531466969 kidney 1.07156965342444 liver 0.713931562449223 stomach 0.180580122529768 testicle -0.0403729318416792 cont.weightedLogRatios: wLogRatio Lung -0.0879889463617628 cerebhem 0.313222289372953 cortex -0.483662691516486 heart -0.226609660682283 kidney 0.185185406400884 liver 0.666129904443455 stomach 0.192961043391355 testicle -0.674827243460046 varWeightedLogRatios=0.358192716643462 cont.varWeightedLogRatios=0.194301446252115 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.35341199175258 0.094813081879335 45.9157313048109 7.11222547279767e-144 *** df.mm.trans1 -0.195165744193249 0.0803056899210621 -2.43028538058873 0.0156294832345491 * df.mm.trans2 -0.255172616828077 0.0803056899210621 -3.17751602755550 0.00162854747694113 ** df.mm.exp2 0.417856919540219 0.111835410090909 3.73635612549327 0.000220597509777318 *** df.mm.exp3 0.0728018500047663 0.111835410090909 0.650973157299527 0.515525212429042 df.mm.exp4 0.115283298508124 0.111835410090909 1.03083002435823 0.303389408590018 df.mm.exp5 0.00624006532240402 0.111835410090909 0.0557968653875512 0.95553802354315 df.mm.exp6 0.0281204345793641 0.111835410090909 0.251444820173732 0.801629622344384 df.mm.exp7 0.362890510329954 0.111835410090909 3.24486233863646 0.00129774496579938 ** df.mm.exp8 0.104331804184821 0.111835410090909 0.932904918934097 0.351563737161938 df.mm.trans1:exp2 -0.433650158340534 0.0968523061813776 -4.47743761029734 1.04801755703446e-05 *** df.mm.trans2:exp2 -0.0645445825262413 0.0968523061813776 -0.666422773716582 0.505615155306972 df.mm.trans1:exp3 -0.14323088607991 0.0968523061813776 -1.47885880808742 0.140149591178657 df.mm.trans2:exp3 -0.0109603919283690 0.0968523061813776 -0.11316603972076 0.909969004369882 df.mm.trans1:exp4 -0.0439977188749553 0.0968523061813776 -0.454276419526446 0.649934060465829 df.mm.trans2:exp4 -0.0433697373533104 0.0968523061813776 -0.447792510713073 0.654602202883627 df.mm.trans1:exp5 0.0224822681602077 0.0968523061813776 0.232129404519338 0.816584055861135 df.mm.trans2:exp5 -0.0361079410868222 0.0968523061813776 -0.37281446885944 0.709530302399759 df.mm.trans1:exp6 -0.0667123424705907 0.0968523061813775 -0.688804893769458 0.491438984702785 df.mm.trans2:exp6 -0.0410954764645378 0.0968523061813776 -0.424310768476461 0.67162078222711 df.mm.trans1:exp7 -0.299788831958542 0.0968523061813776 -3.09531950015853 0.00213749591184547 ** df.mm.trans2:exp7 -0.144628553338593 0.0968523061813776 -1.49328972164838 0.136334511488383 df.mm.trans1:exp8 -0.121622393001634 0.0968523061813776 -1.25575113073579 0.210111025402712 df.mm.trans2:exp8 0.0846016503641387 0.0968523061813776 0.873511986443598 0.383031025319538 df.mm.trans1:probe2 0.809741512507675 0.0484261530906888 16.7211612078963 1.09432950401424e-45 *** df.mm.trans1:probe3 -0.182558409112497 0.0484261530906888 -3.76983091699676 0.000194095705686480 *** df.mm.trans1:probe4 0.780478828248162 0.0484261530906888 16.1168868149931 2.50938882003391e-43 *** df.mm.trans1:probe5 -0.0654195614445839 0.0484261530906888 -1.35091386098894 0.177665674208025 df.mm.trans1:probe6 -0.112826920874872 0.0484261530906888 -2.32987577319177 0.0204266412577073 * df.mm.trans2:probe2 0.179233669218130 0.0484261530906888 3.70117504238826 0.000252107551325893 *** df.mm.trans2:probe3 -0.0373443547522432 0.0484261530906888 -0.771160878344137 0.44117359313702 df.mm.trans2:probe4 0.094462042340372 0.0484261530906888 1.95064105471006 0.0519616286653992 . df.mm.trans2:probe5 -0.132311766462224 0.0484261530906888 -2.73223780989667 0.00663555076694968 ** df.mm.trans2:probe6 -0.105727355036170 0.0484261530906888 -2.18326974761286 0.0297331363662153 * df.mm.trans3:probe2 0.0874297813095122 0.0484261530906888 1.80542487332786 0.0719363445238691 . df.mm.trans3:probe3 0.641020383572288 0.0484261530906888 13.2370701090346 2.85920302018349e-32 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.26855380966494 0.208841666666514 20.4391866709296 3.13010825678748e-60 *** df.mm.trans1 0.0124537656020531 0.176886710077233 0.070405320991133 0.94391449152097 df.mm.trans2 0.0365008861302927 0.176886710077233 0.20635177235393 0.836645748579002 df.mm.exp2 0.137998801470734 0.246336190879678 0.560205144757388 0.575726645561414 df.mm.exp3 -0.317723856265797 0.246336190879679 -1.28979771559830 0.198040771359693 df.mm.exp4 -0.484655486203692 0.246336190879678 -1.96745546999393 0.0499834828321359 * df.mm.exp5 -0.276657849995865 0.246336190879678 -1.12309055769640 0.262230537176584 df.mm.exp6 0.0285917358673125 0.246336190879678 0.116067946675680 0.907670597722857 df.mm.exp7 -0.0488650884853725 0.246336190879678 -0.198367476215626 0.842881970358168 df.mm.exp8 0.0896641774533517 0.246336190879678 0.363991085244749 0.716102078738765 df.mm.trans1:exp2 -0.123901129102023 0.213333399173294 -0.580786363420649 0.561788197442154 df.mm.trans2:exp2 -0.217858104456726 0.213333399173294 -1.02120954947029 0.307917062582599 df.mm.trans1:exp3 0.223622489394254 0.213333399173294 1.04823009552574 0.295313904838822 df.mm.trans2:exp3 0.316984738223433 0.213333399173294 1.48586550184737 0.138287060694324 df.mm.trans1:exp4 0.346192972342573 0.213333399173294 1.62277905702592 0.105609205322045 df.mm.trans2:exp4 0.379997475577742 0.213333399173294 1.78123761703653 0.0758103451106092 . df.mm.trans1:exp5 0.217533599252837 0.213333399173294 1.01968843179652 0.308637033006286 df.mm.trans2:exp5 0.153006654171171 0.213333399173294 0.717218470075943 0.473756038976248 df.mm.trans1:exp6 0.0654631493580594 0.213333399173294 0.306858417911781 0.759148259009161 df.mm.trans2:exp6 -0.109851262281124 0.213333399173294 -0.514927633023324 0.606954694472559 df.mm.trans1:exp7 -0.0199219044354209 0.213333399173294 -0.0933838982204473 0.92565630047838 df.mm.trans2:exp7 -0.086414715062216 0.213333399173294 -0.405068851839838 0.685694217498739 df.mm.trans1:exp8 -0.191071549058531 0.213333399173294 -0.895647609792785 0.371105920510398 df.mm.trans2:exp8 -0.0532686786552038 0.213333399173294 -0.249696854133622 0.802980025335877 df.mm.trans1:probe2 0.033745381352322 0.106666699586647 0.316362852540591 0.751930828629883 df.mm.trans1:probe3 0.124653578405788 0.106666699586647 1.16862693688699 0.243413088679541 df.mm.trans1:probe4 -0.0537373243378588 0.106666699586647 -0.503787260186176 0.614753246720676 df.mm.trans1:probe5 -0.0316215675880719 0.106666699586647 -0.296452104645698 0.767074775324136 df.mm.trans1:probe6 -0.0314295741321021 0.106666699586647 -0.294652166551487 0.768448306279113 df.mm.trans2:probe2 0.057917433676294 0.106666699586647 0.542975773139458 0.587519810147227 df.mm.trans2:probe3 -0.046915982131633 0.106666699586647 -0.439837196739386 0.660348267989593 df.mm.trans2:probe4 0.0876996451549749 0.106666699586647 0.822183919581529 0.411576869784456 df.mm.trans2:probe5 -0.0024025852192678 0.106666699586647 -0.0225242294790995 0.982043649793628 df.mm.trans2:probe6 -0.0867722281627891 0.106666699586647 -0.813489387963135 0.41653493453153 df.mm.trans3:probe2 -0.0647991838147809 0.106666699586647 -0.607492160776415 0.543949869000665 df.mm.trans3:probe3 -0.0635069699179578 0.106666699586647 -0.595377659232534 0.552006676333448